AdvLensPolluter: Object-oriented Adversarial Camera Contamination Attack

The cameras equipped on autonomous vehicles are frequently contaminated by natural occurrences such as mud spots, stain, and raindrops. Such natural contamination is difficult to detect as abnormality. Inspired by this phenomenon, we propose a novel adversarial camera contamination attack that takes advantage of the inconspicuous nature of lens contamination to achieve object-oriented attacks. Our method specifically targets certain objects (e.g., stop signs) while minimizing the impact on untargeted objects (e.g., cars). By designing a contamination generation module, we model various types of contamination and generate adversarial camera patches that are realistic and effective. Our experiments, conducted in both the digital and physical domains, demonstrate that our method can successfully degrade the detection accuracy of target objects by over 60%, while having minimal impact on untargeted objects.